C2INet couples a variational causal intervention with a continually updated prior queue to reduce catastrophic forgetting and improve multi-agent trajectory prediction.
Spatial-Channel Transformer Network for Trajectory Prediction on the Traffic Scenes
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abstract
Predicting motion of surrounding agents is critical to real-world applications of tactical path planning for autonomous driving. Due to the complex temporal dependencies and social interactions of agents, on-line trajectory prediction is a challenging task. With the development of attention mechanism in recent years, transformer model has been applied in natural language sequence processing first and then image processing. In this paper, we present a Spatial-Channel Transformer Network for trajectory prediction with attention functions. Instead of RNN models, we employ transformer model to capture the spatial-temporal features of agents. A channel-wise module is inserted to measure the social interaction between agents. We find that the Spatial-Channel Transformer Network achieves promising results on real-world trajectory prediction datasets on the traffic scenes.
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cs.LG 1years
2024 1verdicts
REJECT 1representative citing papers
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C$^{2}$INet: Realizing Incremental Trajectory Prediction with Prior-Aware Continual Causal Intervention
C2INet couples a variational causal intervention with a continually updated prior queue to reduce catastrophic forgetting and improve multi-agent trajectory prediction.